The disclosed embodiments relate generally to a system and method for improving the operability of a document production environment and, more particularly to an improved approach of forecasting future print-related demand in a document production environment.
Document production environments, such as print shops, convert printing orders, such as print jobs, into finished printed material. A print shop may process print jobs using resources such as printers, cutters, collators and other similar equipment. Typically, resources in print shops are organized such that when a print job arrives from a customer at a particular print shop, the print job can be processed by performing one or more production functions.
In one example of print shop operation, product variety (e.g., the requirements of a given job) can be low, and the associated steps for a significant number of jobs might consist of printing, inserting, sorting and shipping. In another example, product variety (corresponding, for instance, with job size) can be quite high and the equipment used to process these jobs (e.g. continuous feed machines and inserting equipment) can require a high changeover time. Experience working with some very large print shops has revealed that print demand exhibits a tremendous variety of time series behavior. High variability in such large print shop environments can result from large volumes, and may be manifested in what is sometimes referred to as “fat-tailed” or “heavy-tailed” distributions.
Forecasting demand for a given large print shop can be useful in, among other things, managing shop resources. However, traditional approaches of forecasting may be insufficient to adequately characterize high variability demand. In particular, it has been found that the lack of structure associated with a high variability demand can make forecasting difficult, and convergent forecasting results may simply be unattainable for certain high variability demand series when using conventional forecasting techniques.
U.S. Patent Application Publication No. 2007/0070379 A1 to Rai et al. (published on Mar. 29, 2007) discloses an approach for planning print production in a print production enterprise with a neural network having multiple neurons. Each of the neurons is connected to at least one other neuron by a logic connection. As disclosed, the neural network is trained by measuring multiple workflow variables associated print equipment components and assigning a weighting factor to each logic connection. The neural network is updated when a new equipment component is added to the print production enterprise or one of the print equipment components is permanently removed from the print production enterprise. The neural network is also updated when one of the print equipment components is unavailable due to maintenance or repair, or one of the print equipment components is unavailable due to a prior commitment to another print job. Further detailed discussion of neural networks is provided in U.S. Pat. No. 7,092,922 to Meng et al. The pertinent portions of the '922 patent, along with the pertinent portions of the '0379 publication, are incorporated herein by reference.
The '0379 publication may be viewed as employing a causal forecasting model to improve print production planning. That is, the neural network is created with print production related measurements for one or more workflow variables associated with one or more print jobs. While causal forecasting appears well suited for the intended purpose of planning print shop production, it might be difficult to apply the same type of forecasting to the area of forecasting print-related demand. More particularly, many of the causal factors associated with forecasting print-related demand are typically not within the direct control of print production enterprise managers, particularly when the print production enterprise is dispersed throughout a network. Consequently, to achieve causal forecasting for a sizable print production enterprise, a considerable amount of information would have to be obtained from the customers, possibly through surveys.
In one aspect of the disclosed embodiments there is disclosed a print demand forecasting system for use with a print production system in which multiple print jobs are processed over a selected time interval. The print demand forecasting system includes: a data collection tool, said data collection tool collecting print demand data for each print job processed during the selected time interval; mass memory for storing the collected print demand data; and a computer implemented service manager for processing the stored print demand data to obtain a first demand series with two or more demand components and a second demand series with one demand component, each one of the two or more demand components being less than a selected variability level and the one demand component being greater than the selected variability level, said computer implemented service manager being adapted to (1) generate a first demand related forecast with a combination of the two or more demand components, and (2) generate a second demand related forecast with the one demand component. As contemplated, the second demand related forecast is generated with a neural network. The neural network includes a layer including a plurality of neurons. Each one of the plurality of neurons is weighted, and the weighting of each one of the plurality of neurons is optimized with respect to a set of print-related demand data collected over one or more selected time intervals. The plurality of neurons corresponds with a number that is optimized to improve accuracy of forecasting. The number is re-optimized after a selected time interval that varies as a function of a document production application dictating demand forecasting.
In another aspect of the disclosed embodiments there is disclosed a system of forecasting print-related demand in a document production environment. The print-related demand forecasting system includes: a processor; and a processor readable storage medium in communication with the processor, the processor readable storage medium containing one or more programming instructions for: providing a hidden layer including a plurality of neurons and causing each one of the plurality of neurons, corresponding with a number, to be weighted with a first set of print-related demand data, optimizing the number of neurons to improve accuracy of forecasting, providing an input layer including a plurality of inputs, the plurality of inputs communicating with the plurality of neurons of the hidden layer, communicating a second set of print-related demand data to the plurality of inputs of the input layer, the second set of print-related demand data corresponding with a demand series obtained from the document production environment, re-optimizing the number of neurons after a selected time interval elapses, where the selected time interval varies as a function of a document production application dictating demand forecasting, and responsive to communicating the second set of print-related demand data to the plurality of inputs, a print-related demand forecast output for the document production environment is generated with the plurality of weighted neurons of the hidden layer.
In yet another aspect of the disclosed embodiment there is disclosed a method of optimizing a print-related demand forecasting system. The optimizing method includes: providing a hidden layer including a plurality of neurons and causing each one of the plurality of neurons to be weighted with a first set of print-related demand data, wherein the plurality of neurons correspond with a number; optimizing the number of neurons to improve accuracy of forecasting; providing an input layer including a plurality of inputs, the plurality of inputs communicating with the plurality of neurons of the hidden layer; communicating a second set of print-related demand data to the plurality of inputs of the input layer, the second set of print-related demand data corresponding with a demand series obtained from the document production environment; re-optimizing the number of neurons after a selected time interval elapses, wherein the selected time interval varies as a function of a document production application dictating demand forecasting; and, responsive to communicating the second set of print-related demand data to the plurality of inputs, generating a print-related demand forecast output for the document production environment with the plurality of weighted neurons of the hidden layer.